Overview

The dataset provides detailed information about the stable isotope composition of different precipitation types (rainf, fog, throughfall). It was manually collected on up to 9 study plots on a generally weekly basis between November 2012 and November 2014. The following map shows the distribution of the study plots on the southern slopes of Mt. Kilimanjaro.

The study plots span across an altitude gradient rising from 950 m to nearly 4,000 m a.s.l. The plot IDs are the ones used within the respective research group.

PlotID Land cover
fer0 Forest Erica
fpd0 Forest Podocarpus disturbed
fpo0 Forest Podocarpus
foc0 Forest Ocotea
foc6 Forest Ocotea
flm1 Forest lower mountain
nkw1 Open area near field station
hom4 Homegarden
sav5 Savanna

For further details see section Publication at the end of the document.

mapviewOptions(basemaps = "Esri.WorldImagery")
mapview(idata, zcol = "PlotID", legend = TRUE)

Datasets

Precipitation was sampled from rain gauges, fog mesh grids and throughfall installations on the study plots. Throughfall has been measured by many gauges placed across the study plot. These gauges are named “Bnn” with nn being an integer number in the field and lab records. To get the mean throughfall of the respective time slot, the gauge data must be averaged.

Sampling was carried out manually by local field staff and recorded on paper sheets. Sampling took place about every week. Two intensive sampling campaigns with multiple daily recordings took place in December 2013 and April 2014.

Folder Data
data/field_records Scans of the original paper sheets group by study plots
data/lab_records Digitized paper sheets and isotope analysis data
data/compiled_data Comprehensive dataset as Shapefile and CSV

The data in the compiled_data folder does not include the intensive campaigns.

The structure of the compile dataset is as follows:

## Simple feature collection with 6 features and 12 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 310260.9 ymin: 9659338 xmax: 310260.9 ymax: 9659338
## projected CRS:  WGS 84 / UTM zone 37S
##   PlotID       Date     Time      Season PrcpTyp Elvton PrcpAmt   dO18 sddO18
## 1   fer0 2012-11-22 15:40:00 short rains     fog   3880   0.000     NA     NA
## 2   fer0 2012-11-29 14:15:00 short rains     fog   3880   0.000     NA     NA
## 3   fer0 2012-12-06 14:10:00 short rains     fog   3880   1.150 -7.310  0.050
## 4   fer0 2012-12-12 14:15:00 short rains     fog   3880   0.000     NA     NA
## 5   fer0 2012-12-20 15:10:00 short rains     fog   3880   0.000     NA     NA
## 6   fer0 2012-12-28 16:17:00 short rains     fog   3880   3.598 -7.071  0.094
##        dD   sdD dExcess                 geometry
## 1      NA    NA      NA POINT (310260.9 9659338)
## 2      NA    NA      NA POINT (310260.9 9659338)
## 3 -36.600 0.350   21.88 POINT (310260.9 9659338)
## 4      NA    NA      NA POINT (310260.9 9659338)
## 5      NA    NA      NA POINT (310260.9 9659338)
## 6 -35.258 0.222   21.31 POINT (310260.9 9659338)

The variables have the following meaning:

Column Content
PlotID Study plot ID as used within the research group
Date Date of the observation
Time Time of the observation
Season Type of rainy season
PrcpType Type of precipitation (rain, fog, tf = throughfall)
Elvton Elevation of the study plot in m a.s.l
PrcpAmt Amount of recorded precipitation (rain, fog or throughfall)
dO18 delta 18O/16O
sddO18 standard deviation of delta 18O/16O
dD delta D/H
sddD standard deviation of delta D/H
dExcess Deuterium excess

Examples

The following figures shows the dO18 and dD values of the compiled dataset. The light grey line illustrates the global meteoric water line and the black line the respective local meteoric water line for all (top left), fog (top right), rain (bottom left) and throughfall (bottom right) samples.

facet_idata <- st_drop_geometry(idata)
facet_idata$facet <- facet_idata$PrcpTyp
facet_idata <- rbind(facet_idata, data.frame(st_drop_geometry(idata), facet = "all"))

ggplot(facet_idata, aes(x = dO18, y = dD)) + 
  geom_point(aes(color = PlotID, shape = PrcpTyp)) + 
  geom_abline(intercept = 10, slope = 8, color = "darkgrey") +
  geom_smooth(method = "lm", se=FALSE, color = "black") +
  facet_wrap(vars(facet)) +
  scale_color_manual(values = plotcolors) + 
  theme_bw()

The following figures shows the seasonal dynamics of the dO18 and dExcess rainfall sample values of the compiled dataset.

ylim_1 <- c(0, 450)
ylim_2 <- c(-10, 35)  

b <- diff(ylim_1)/diff(ylim_2)
a <- b * (ylim_1[1] - ylim_2[1])

ggplot(idata[idata$PrcpTyp == "rain",], aes(x = Date, y = PrcpAmt)) + 
  geom_bar(stat = "identity", color = "black") + 
  geom_line(aes(y = a + b * dO18), color = "blue") +
  geom_point(aes(y = a + b * dO18), color = "blue") + 
  geom_line(aes(y = a + b * dExcess), color = "red") +
  geom_point(aes(y = a + b * dExcess), color = "red") + 
  scale_y_continuous("Rainfall (mm)", sec.axis = sec_axis(~ (. - a)/b, name = "delta 18O, d excess")) +
  facet_wrap(vars(PlotID), ncol = 2) +
  theme_bw()

Funding

The research was fundet by the German Research Foundation (DFG) as part of the Research Unit 1246 - Kilimanjaro ecosystems under global change (Na 783/5-2).